Supervised Canonicalization: 
Datasets such as ShapeNet [1]} and ModelNet40 [2]} have 3D shapes that are manually pre-canonicalized.
This inductive bias aids category-level generalization in problems such as 3D reconstruction [3]}, [4]}, [5]}, [6]}.
We can also use these datasets to formulate canonicalization as a supervised learning problem [7]} enabling applications ranging from 6 degree-of-freedom object pose estimation, multi-view reconstruction [8]}, [9]}, and reconstruction of articulating [10]}, [11]} and non-rigid objects [12]}.
However, our goal is to canonicalize using without using manual pose labels.
